To deal with low-contrast and high-noisy natural images, an image enhancement method based on internal generative mechanism (IGM) and improved pulse coupled neural network (PCNN) is proposed. First, the original image is decomposed into rough sub-graph and detail sub-graph by the theory of IGM. And then, an improved PCNN method is adopted to make the rough sub-graph more clearly. At the same time, the enhancement method which PCNN incorporates with fuzzy sets is introduced for the detail sub-graph so as to sharpen the image edge and remove outliers. Finally, the final image is reconstructed by processed rough sub-graph and detail sub-graph. Experimental results show that the proposed algorithm can effectively enhance the image contrast and image contour, as well as filter out some noise without any loss of image edges.
Image enhancement using IGM and improved PCNN
First published at:Sep 15, 2017
Opto-Electronic Engineering Vol. 44, Issue 09, pp. 888 - 894 (2017) DOI:10.3969/j.issn.1003-501X.2017.09.005
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Get Citation: Zhang Qian, Zhou Pucheng, Xue Mogen, et al. Image enhancement using IGM and improved PCNN[J]. Opto-Electronic Engineering, 2017, 44(9): 888–894.
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